Data Origins

The raw data for this project was obtained from Integrated Carbon Observation System on November 29, 2024, and covers one-year periods from 1990 to 2020. The data includes the year, country and fossil emissions, in order to facilitate observations only the data from the tab Consumption Emissions was utilized.

It is important to note that all consumption emission values are in million tonnes of carbon per year.

The link of the data set can be found below.

Data Source

If you are going into the Integrated Carbon Observation System main webpage you must click on the following tabs: Science & Impact< Global Carbon Budget< Global Carbon Budgget 2022<View the Global Carbon Budget 2022 here< 2022 National Fossil Carbon Emissions 2022 v.1.0

https://www.icos-cp.eu/science-and-impact/global-carbon-budget/2022

Background

Consumption emission gases are those that are produced when there is usage of combustion fuel sources, such as the gases expelled by cars.

Due to the human industrialization advancements there has been a heavy reliance on combustion fuel that impacts the greenhouse effect of the Earth.

This, consequently influences other aspects of Earth, for example the rising temperatures, sea levels rising, the proliferation of forest fires, among others.

Research Question

The quantity of consumption emission has skyrocketed from 1990 to 2020?

The visualization aims to examine the consumption CO₂ emissions over a 30-year period. It is hypothesized that these emissions will have increasingly noticeable changes as time progresses due to an extreme dependence on fossil fuel combustion.

Data Preparation

Loading packages

The packages used in this project are the following:

#Import packages
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
library(plotly)
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
library(readxl)
library(tidyr)
library(here)
## here() starts at C:/Users/gabyd/OneDrive/Documents/R project/R-Final-Project
library(htmlwidgets)

Importing data

#load the data using the file path, sheet = 3 selects the tab you want to use and skip = 8 indicates to R Studio that we want to eliminate the first 8 rows of information

data <- read_excel(here("data", "emissions.xlsx"), sheet = 3, skip = 8)

Reshape your data

#this will create Year, Country and Emissions columns
longy_data <- pivot_longer(
     data, 
     cols = -Year,           
     names_to = "Country",   
     values_to = "Emissions")

head(longy_data)
## # A tibble: 6 × 3
##    Year Country     Emissions
##   <dbl> <chr>           <dbl>
## 1  1990 Afghanistan     NA   
## 2  1990 Albania          1.52
## 3  1990 Algeria         NA   
## 4  1990 Andorra         NA   
## 5  1990 Angola          NA   
## 6  1990 Anguilla        NA

Define your variables

data_cleaned <- longy_data %>%
filter(Country == "Asia"|
Country == "Africa"|
Country == "Central America"|
Country == "Europe"|
Country == "Middle East"|
Country == "North America"|
Country == "Oceania"|
Country == "South America"|
Country == "World")

data_cleaned <- data_cleaned  %>% filter(Year != 2021)

Plot the data

p <-ggplot(data_cleaned, aes(x = Year, y = Emissions, group = Country))
p + geom_line()

Add color by country, transparency, adjust line width, tittle, subtittle and caption

ggplot(data_cleaned, aes(x = Year, y = Emissions, group = Country, color = Country)) +
     geom_line(linewidth = 0.7, alpha = 0.7) +  
     theme_minimal() +
     labs(
         title = "Global Emission Trends by Continent",
         subtitle = "Shown is the carbon emissions in million tonnes of carbon every ten years.",
         caption = "Source: Integrated Carbon Observation System",
         x = "Year",
         y = "Emissions",
         color = "Legend")

Save the plot in order to make it interactive

gg <- ggplot(data_cleaned, aes(x = Year, y = Emissions, group = Country, color = Country)) +
     geom_line(linewidth = 0.7, alpha = 0.7) +  
     theme_minimal() +
     labs(
         title = "Global Emission Trends by Continent",
         subtitle = "Shown is the carbon emissions in million tonnes of carbon every ten years.",
         caption = "Source: Integrated Carbon Observation System",
         x = "Year",
         y = "Emissions",
         color = "Legend")

Convert to plotly object

interactive_plot <- ggplotly(gg) %>%
  layout(
    annotations = list(
      list(
        x = 0.5,
        y = 1.02,
        text = "Shown is the carbon emissions in million tonnes of carbon every ten years.",
        showarrow = FALSE,
        xref = "paper",
        yref = "paper",
        font = list(size = 12))))

Ensure it works on R Markdown

saveWidget(as_widget(interactive_plot), "interactive_plot.html", selfcontained = TRUE)

Make the graph interactive

ggplotly(gg) %>%
  layout(
    margin = list(b = 100, l = 100),  # Adjust bottom and left margins for axis labels
    xaxis = list(
      title = list(
        text = "Year",  # Add custom x-axis title
        font = list(size = 14)
      )
    ),
    yaxis = list(
      title = list(
        text = "Emissions (million tonnes)",  # Add custom y-axis title
        font = list(size = 14)
      )
    ),
    annotations = list(
      # Add the main subtitle
      list(
        x = 0.5,
        y = 1.05,
        text = "Shown is the carbon emissions in million tonnes of carbon every ten years.",
        showarrow = FALSE,
        xref = "paper",
        yref = "paper",
        font = list(size = 12)
      ),
      # Add the caption as a separate annotation
      list(
        x = 0,
        y = -0.2,  # Position it below the graph
        text = "Source: Integrated Carbon Observation System",
        showarrow = FALSE,
        xref = "paper",
        yref = "paper",
        xanchor = "left",  # Align to the left
        font = list(size = 10, color = "gray"))))

Conclusions

There is a clear increase in the CO2 consumption emissions from the years 2000 to 2010, specifically of the World and Asia regions, you can observe that these areas continue to show increasing emissions until 2019 at which point there is a notable decrease in both.

In contrast, Oceania, Middle East, Africa, South America, and Central America have the lowest emissions with a slight increase in CO2 between 2002 and 2020.

North America and Europe have fairly stable emissions with the decade from 2010 to 2020 showing a slight decrease in their emissions.

Overall in can be affirmed that whilst the majority of the regions experienced an increased from 1990 to 2019, there is a worldwide decrease from 2019 to 2020.

This can be attributed to the population’s awareness and actions that there are consequences to our abusive usage of combustion fuels.

Reference

Pierre Friedlingstein, Michael O’Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson,Simone Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O´Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, Bo Zheng. (2022). Global Carbon Budget. Integrated Carbon Observation System. Sci. Data. https://doi.org/xxxxx

Peters, G., Minx, J., Weber, C., and Edenhofer, O. (2011). Growth in emission transfers via international trade from 1990 to 2008. Proceedings of the National Academy of Sciences. 108, 8903-8908. http://www.pnas.org/content/108/21/8903.abstract